Cloud fraction retrieval using data from Indian geostationary satellites and validation

ABSTRACT

Cloud fraction (CF) is known as the dominant modulator of Earth’s radiation budget, thus regarded as Essential Climate Variable. CF is retrieved using Indian geostationary satellites Kalpana-1 and Indian National Satellite System (INSAT-3D) by calculating the fraction of area covered by the clouds in a given pixel divided by the total area of the pixel. The technique uses multi-channel thresholding for three channels in Kalpana-1, that is, thermal, visible, and water vapour, and four channels in INSAT-3D with mid-infrared channel in addition to the three mentioned for Kalpana-1. A 2-year record of CF at 30-min intervals was generated for the Indian region using the Kalpana-1 and INSAT-3D data. The retrieved CF data were compared against Moderate Resolution Imaging Spectroradiometer (MODIS) CF product in the near vicinity of simultaneous data availability (i.e., within ±15 min interval). This product agrees with MODIS (correlation coefficient 80%) with a root mean square error (RMSE) of 0.30, in spite of ±15 min of time difference between both the satellites. In addition, ground-based Total Sky Imager (TSI-440) retrieved data over Pune is used to validate the satellite retrieved CF over the same region. The probability of detection between retrieved CF and ground-based data is relatively more for range of CF between 0.00 and 0.25, that is, 90% and more than 20% for CF greater than 0.50. In view of the close agreement between retrieved CF from Kalpana-1 and INSAT-3D with MODIS and TSI-440, this product is operational and is being made available through National Information System for Climate and Environment Studies portal for use in better understanding of climate.     

Characterization of the tropical diurnal fire cycle using VIRS and MODIS observations

Seven years of data from the Tropical Rainfall Measuring Mission (TRMM) Visible and Infrared Scanner (VIRS) were used to characterize the average diurnal fire cycle in 15 regions of the tropics and sub-tropics. Bias errors in the resulting diurnal cycles were either avoided or removed through a combination of judicious region selection and the application of corrections to compensate for cloud obscuration and time-dependent “blind spots” in the fire-detection capability of the VIRS sensor. Supplementary data from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board NASA's Terra satellite aided this process. In all regions, the local time of peak burning fell between 13:00 and 18:30, with fire activity peaking distinctly earlier for the heavily forested regions. The time period of the central 50% of total daily fire activity varied from a minimum of 1.3 h in North Central Africa to a maximum of 5.5 h in Eastern Australia. In general, shorter periods of burning were associated with greater tree cover. Using the diurnal cycles obtained for each region, an analysis of the drift in the local overpass times of the NOAA-7 through NOAA-14 afternoon satellites was performed. Results show that very large, spurious trends are likely to occur in a long-term Advanced Very High Resolution Radiometer (AVHRR) fire record due to differences in diurnal sampling over time.     

Comparing the utility of microwave and thermal remote-sensing constraints in two-source energy balance modeling over an agricultural landscape

A two-source (soil + vegetation) energy balance model using microwave-derived near-surface soil moisture as a key boundary condition (TSM_SM) and another scheme using thermal-infrared (radiometric) surface temperature (TSM_TH) were applied to remote sensing data collected over a corn and soybean production region in central Iowa during the Soil Moisture Atmosphere Coupling Experiment (SMACEX)/Soil Moisture Experiment of 2002 (SMEX02). The TSM_SM was run using fields of near-surface soil moisture from microwave imagery collected by aircraft on six days during the experiment, yielding a root mean square difference (RMSD) between model estimates and tower measurements of net radiation (Rn) and soil heat flux (G) of approximately 20 W m^− 2, and 45 W m^− 2 for sensible (H) and latent heating (LE). Similar results for H and LE were obtained at landscape/regional scales when comparing model output with transect-average aircraft flux measurements. Flux predictions from the TSM_SM and TSM_TH models were compared for two days when both airborne microwave-derived soil moisture and radiometric surface temperature (T_R) data from Landsat were available. These two days represented contrasting conditions of moderate crop cover/dry soil surface and dense crop cover/moist soil surface. Surface temperature diagnosed by the TSM_SM was also compared directly to the remotely sensed T_R fields as an additional means of model validation. The TSM_SM performed well under moderate crop cover/dry soil surface conditions, but yielded larger discrepancies with observed heat fluxes and T_R under the high crop cover/moist soil surface conditions. Flux predictions from the thermal-based two-source model typically showed biases of opposite sign, suggesting that an average of the flux output from both modeling schemes may improve overall accuracy in flux predictions, in effect incorporating multiple remote-sensing constraints on canopy and soil fluxes.     

Verification of dust forecast over the Indian region with satellite and ground based observations

This article presents the verification results of the dust forecast by a numerical model over India and neighbouring regions. National Centre for Medium Range Weather Forecasting Unified Model (NCUM) is a global numerical weather prediction (NWP) model with a prognostic dust scheme. Evaluation of the performance of dust forecast by NCUM is carried out in this study. Model forecast of dust optical depth (DOD) at 550 nm is validated against ground-based and satellite observations since optical depth measurements in mid-visible wavelength are easily available. Daily 5-day forecast based on 00 UTC initial condition during dust dominated pre-monsoon season (April–May) of 2014 is used in this study. Location specific and geographical distribution of dust forecast is validated against Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite retrieved DOD observation at 532 nm, Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD), Ozone Monitoring Instrument (OMI), aerosol index, and Aerosol Robotic Network (AERONET) station data of total and coarse mode AOD. The verification results indicate that NCUM dust forecast generally gives good representation of large scale geographical distribution of dust over the western region of India. DOD forecasts show good correlation with co-located CALIPSO DOD over the western part (0.71) compared to central (0.58) and eastern (0.61) part of India in April while it show similar trend in May with slightly improved correlation (0.68) over the eastern part of India. Results also show that DOD forecasts are better correlated to AERONET coarse mode AOD observations over Jaipur in April and over Kanpur in May. Vertical distribution of dust concentrations in the forecast show reasonably good agreement with attenuated backscatter and depolarization ratio from CALIPSO observations. The model is also able to simulate spatiotemporal distribution of dust during a major dust event as observed by CALIPSO, MODIS, and OMI.     

Long-term trends and decadal solar variability in ozone near the tropopause over the Indian region

To investigate the long-term trends and effects of decadal solar variability in the upper tropospheric ozone, data obtained from the Stratospheric Aerosol and Gas Experiment II (SAGE II) aboard the Earth Radiation Budget Satellite (ERBS) during the period 1985–2005 were analysed using a multifunctional regression model over the Indian region (8–40° N; 65–100° E). Analysis of time series spanning these years shows statistically insignificant trends (at the two-sigma level (95% confidence level)) at upper tropospheric pressure levels (10?16 km). This period covers two solar cycles, one lasting from 1985 to 1995 and the other from 1996 to 2005; these are referred to as decade I and decade II, respectively. Since temporal variation in ozone number density indicates 11 year periodicity, trends are statistically significant when calculated separately during each solar cycle. Trend analysis indicates statistically significant positive trends (0.7 ± 1.7% to 3.9 ± 2.9% year^?1 during decade I, and 2.2 ± 1.6% to 4.5 ± 3.0% year^?1 during decade II). In general, higher ozone trends are observed during decade II. Seasonal variation in trends during decade II shows increasing trends during the pre-monsoon (0.8?3.8% year^?1), monsoon (0.8?7.1% year^?1), and post-monsoon (2.8?8.0% year^?1) seasons. The annually averaged solar signal in ozone is found to be of the order of around??5 ± 4.3% to??13.8 ± 6.7%/(100 sfu). Results obtained in the present study are also compared with those obtained by other researchers.     

Modelling surface energy fluxes over maize using a two-source patch model and radiometric soil and canopy temperature observations

Models estimating surface energy fluxes over partial canopy cover with thermal remote sensing must account for significant differences between the radiometric temperatures and turbulent exchange rates associated with the soil and canopy components of the thermal pixel scene. Recent progress in separating soil and canopy temperatures from dual angle composite radiometric temperature measurements has encouraged the development of two-source (soil and canopy) approaches to estimating surface energy fluxes given observations of component soil and canopy temperatures. A Simplified Two-Source Energy Balance (STSEB) model has been developed using a “patch” treatment of the surface flux sources, which does not allow interaction between the soil and vegetation canopy components. A simple algorithm to predict the net radiation partitioning between the soil and vegetation is introduced as part of the STSEB patch modelling scheme. The feasibility of the STSEB approach under a full range in fractional vegetation cover conditions is explored using data collected over a maize (corn) crop in Beltsville Maryland, USA during the 2004 summer growing season. Measurements of soil and canopy component temperatures as well as the effective composite temperature were collected over the course of the growing season from crop emergence to cob development. Comparison with tower flux measurements yielded root-mean-square-difference values between 15 and 50 W m^− 2 for the retrieval of the net radiation, soil, sensible and latent heat fluxes. A detailed sensitivity analysis of the STSEB approach to typical uncertainties in the required inputs was also conducted indicating greatest model sensitivity to soil and canopy temperature uncertainties with relative errors reaching ∼ 30% in latent heat flux estimates. With algorithms proposed to infer component temperatures from bi-angular satellite observations, the STSEB model has the capability of being applied operationally.     

Evaluation of INSAT-3D derived TPW with AIRS retrievals and GNSS observations over the Indian region

ABSTRACT

Precipitable water vapor is an important and highly atmospheric variable in temporal and spatially; the knowledge of its variability is important for meteorological and climatological studies. The main objective of this paper, an evaluation of Total Precipitable Water (TPW) retrieved from the Indian National Satellite System (INSAT-3D) data provided by the INSAT-3D Meteorological Data Processing System (IMDPS) at National Satellite Meteorological Centre (NSMC), New Delhi along with collocated Atmospheric Infrared Sounder (AIRS) L3 Standard Physical Retrievals (AIRS-only) during a one year period 2017 over the Indian region. The spatiotemporal distribution of seasonal mean and monthly dependency of the correlation coefficient, bias and root mean square error (RMSE) was computed between INSAT-3D TPW and AIRS retrievals during both daytime and nighttime. The results of the intercomparison reveal that TPW from the INSAT-3D is in very good agreement with the AIRS, that is seasonally distribution of TPW larger in warm seasons (June, July, August) and smaller in the cold season (December, January, February) and monthly dependency of correlation coefficient (> 0.8), bias (2–3 mm) and RMSE (< 5 mm) in all months during both daytime and nighttime, except June, July, August over coastal regions of Arabian Sea and Bay of Bengal shows degradation performance. However, the statistical analysis between INSAT-3D with respect to AIRS TPW retrieval during both daytime and nighttime shows that more reliable except during cloudy days. In addition to it, a similar analysis is carried out to assess the relative performance of INSAT-3D retrieved TPW with respect to 10 Global Navigational Satellite System (GNSS) over the Indian subcontinent obtained from the NSMC the period from 1^st January to 30 June 2017 on hourly. In this analysis, for each station time series, diurnal variations of TPW and monthly, seasonal distribution of the Taylor diagram was carried out between INSAT-3D and GNSS retrievals. INSAT-3D and 10 GNSS stations gave comparable accuracies during the months of March to June whereas the quality degrades in January and February months resulting in slight error. It might be caused by the in the winter season the surface emissivity could be one region for more degraded performance under the drier condition it brings in more uncertainties in surface emissivity. Overall, these results give good confidence in the quality and potential of INSAT-3D over the Indian region and can be used in weather forecasting and nowcasting applications.     

INSAT-3D and MODIS retrieved sea surface temperature validation and assessment over waters surrounding the Indian subcontinent

The INSAT-3D imager (4 km) and Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on-board Aqua and Terra space-platforms level-2 (1 km) sea surface temperature (SST_skin) product accuracy has been analysed over waters surrounding the Indian subcontinent by indirect comparison method using collocated bulk in-situ measurements (SST_depth) for 3 years (October 2013–October 2016). Statistical results show that root mean square error of all the three satellites is in range of around 0.60–0.70°C. Retrieval error is found to be slightly more in case of validation against iQuam data set. INSAT-3D is showing more underestimation with bias ranging from about ?0.16°C to ?0.20°C than MODIS sensor having bias in range of about 0.06°C to ?0.12°C. All the three missions are slightly underestimating over open-ocean with bias ranging in 0–0.17°C. INSAT-3D is significantly underestimating in-situ observations over the Arabian Sea (approximate bias = 0.27°C). Seasonal validation analysis reveals relatively high retrieval error during monsoon season than pre-monsoon and post-monsoon seasons. MODIS sensor is showing significant underestimation during monsoon with bias ranging from approximately ?0.29°C to ?0.58°C. Overall, all the three missions are performing similarly well over the study area.     

Derivation and validation of the seasonal thermal structure of Lake Malawi using multi-satellite AVHRR observations

Lake Malawi is the second largest lake in Africa by volume and an important regional source of food. Seasonal fluctuations in the primary production of the lake are principally controlled by the lake's thermal structure, which modulates the mixing of nutrient-rich deep water with that of the phytoplanktonrich near-surface layer. Satellites potentially offer an efficient, low cost method of providing information on the lakes thermal structure over the longer term via remote sensing observations of lake surface temperature. Here we investigate the accuracy of remotely sensed lake surface temperatures derived using data from the NOAA-11 AVHRR over a two-year period (1992-1993). Optimised triple window atmospheric correction algorithms are shown to provide an accuracy of around 0.5°C when compared to in situ     

Instantaneous near-surface level humidity from ERS-1/ATSR observations: a case study in the Indian Ocean

A relation between near surface level specific humidity (q_a) and ERS-1/Along Track Scanning Radiometer's nadir and forward scan observations is proposed on the basis of radiative transfer simulations for tropical atmospheres, The simulation study shows that q_a can be estimated within an r.m.s. error of 15 per cent when it is compared with the values derived from ship observations. As a case study analysis, we have applied the relation to an actual ERS-1/ATSR data dated 3 March 1992. The computed near-surface level specific humidity compared well with values derived from simultaneous ship observations and the r.m.s. error was found to be within 15 per cent.     

Exploration of the potential for using Spark-I observations to derive atmospheric parameters

As part of a low-cost nanosatellite constellation, Spark-I, a hyperspectral resolution instrument with 149 channels covering blue to near-infrared, has been successfully launched on 22 December 2016. In this study, we try to explore its potential to derive atmospheric parameters according to optimal estimation theory. From simulated measurements, we estimated the information content described as degrees of freedom for signal (DFS) and error reduction of typical aerosols, including dust, soot, sea salt, and sulphate, as well as water vapour (H₂O), nitrogen dioxide (NO₂), surface pressure, and Sun-induced fluorescence (SIF). Based on the results, only the H₂O column, aerosol optical depth (AOD), and surface albedo could be derived with error reduction of 20%, 40%, and near 100%, respectively. The retrieval of aerosol was strongly correlated with surface albedo, especially dust, with a DFS of 0.3–0.9 due to surface variations. After investigating the impact of the oxygen and H₂O absorption bands on the aerosol information content, we recommend retrieving aerosol characteristics using full channels, rather than sub-band channels. The more fraction one type of aerosol is, the larger information about it we can get. Different AOD results in 0.3–0.8 aerosol DFS change and solar zenith angle influences less. The information of atmospheric gases was sensitive to both signal-to-noise ratio (SNR) and spectral resolution due to their particular absorption patterns. However, improving only the SNR by double or more could allow for the derivation of SIF emissions, assuming the a priori estimation is accurate.     

Bimodal variation of SST and related physical processes over the North Indian Ocean: special emphasis on satellite observations

Using sea surface temperature (SST) and wind speed retrieved by the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), for the period of 1998–2003, we have studied the annual cycle of SST and confirmed the bimodal distribution of SST over the north Indian Ocean. Detailed analysis of SST revealed that the summer monsoon cooling (winter cooling) over the eastern Arabian Sea (Bay of Bengal) is more prominent than winter cooling (summer monsoon cooling). A sudden drop in surface short wave radiation by 57 W m^?2 (74 W m^?2) and rise in kinetic energy per unit mass by 24 J kg^?1 (26 J kg^?1) over the eastern Arabian Sea (Bay of Bengal) is observed in summer monsoon cooling period. The subsurface profiles of temperature and density for the spring warming and summer monsoon cooling phases are studied using the Arabian Sea Monsoon Experiment (ARMEX) data. These data indicate a shallow mixed layer during the spring warming and a deeper mixed layer during the summer monsoon cooling. Deepening of the mixed layer by 30 to 40 m with corresponding cooling of 2°C is found from warming to summer monsoon cooling in the eastern Arabian Sea. The depth of the 28°C isotherm in the eastern Arabian Sea during the spring warming is 80 m and during summer monsoon cooling it is about 60 m, while over the Bay of Bengal the 28°C isotherm is very shallow (35 m), even during the summer monsoon cooling. The time series of the isothermal layer depth and mixed layer depth during the warming phase revealed that the formation of the barrier layer in the spring warming phase and the absence of such layers during the summer cooling over the Arabian Sea. However, the barrier layer does exist over the Bay of Bengal with significant magnitude (20–25 m). The drop in the heat content with in first 50 m of the ocean from warming to the cooling phase is about 2.15 × 10⁸ J m^?2 over the Arabian Sea.     

Intraseasonal signals in the daily high resolution blended Reynolds sea surface temperature product over the tropical Indian Ocean and their validation

National Oceanic and Atmospheric Administration daily sea surface temperature (SST) products based on Advanced Microwave Scanning Radiometer (AMSR) and Advanced Very High Resolution Radiometer (AVHRR) have been used to understand the variability in the tropical Indian Ocean SST. These products are comparable with the deep sea moored buoy observations and the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) SST in the tropical Indian Ocean. However considerable difference is noticed between these satellite SST products and deep sea buoys, especially at the intraseasonal time scale. Further the first Complex Empirical Orthogonal Function (CEOF) mode of TMI and AVHRR SST explains respectively 46.49% and 46.19% of the total variance. The second CEOF mode of TMI and AVHRR SST explains respectively 23.19% and 18.94% of the total SST variance in the tropical Indian Ocean. The AVHRR SST product is important because this daily product has been available since 1985. The analysis shows that AMSR measurements are contributing considerably to the understanding of the tropical Indian Ocean SST variability. Though satellite SST products are able to capture the observed intraseasonal variability reasonably well, more accurate satellite SST products are therefore necessary to understand the climatologically important Indian Ocean region and its air–sea interaction processes.     

Assessing the distribution and abundance of seabed minerals from seafloor photographic data in the Central Indian Ocean Basin

Distribution characteristics of deep-sea mineral resources such as polymetallic nodules and ferromanganese crusts are often influenced by local seafloor features such as the topographic undulations and sediment thickness. Qualitative as well as semi-quantitative estimation of these resources have been attempted earlier by using seafloor photographic data. This study analyses the distribution of these minerals in relation to their associated seafloor surface features and develops empirical formulae for abundance estimation of a deposit from photographic data in conjunction with sounding and sampling data in the Central Indian Ocean Basin. Data from >20,000 photos were analysed and correlated with geological features such as bathymetry, sediment thickness, and sediment physical properties. The study shows that nodule populations are negatively correlated with the thickness of the acoustically transparent layer (ATL), which in turn is controlled by the topographic undulations. The variable burial of nodules under the ATL, as indicated by sampling data, leads to underestimation of nodules from photographs. In order to improve the accuracy of such estimations, different constant factors (C-factor) are proposed for use in empirical formulae depending upon the distribution characteristics of the deposits.     

Modelling the impact of land-cover change on potential soil loss in the Taita Hills,Kenya, between 1987 and 2003 using remote-sensing and geospatial data

In sub-Saharan Africa, natural vegetation is being transformed into agricultural lands at a fast rate, endangering ecosystem services and increasing soil-loss potential, which may trigger land degradation. For the Taita Hills study area in Kenya, multi-temporal land-cover models of 1987, 1999 and 2003, derived from Satellite Pour l'Observation de la Terre (SPOT) imagery using a multi-scale segmentation/object relationship modelling (MSS/ORM) methodology and a rainfall layer, a digital elevation model (DEM) and a digital soil map were applied to model potential soil loss. Population growth in the area has led to a shortage of agricultural land and movement of people to the lowlands, evidenced by a 39% (9.3 km²) increase in croplands from 30% to 41% of the study area during the research time frame. Expansion took place mostly in surrounding foothills and lowlands, at the expense of natural shrubland and grassland, but also occurred in the hills. Universal soil-loss equation (USLE) model results showed a 60% (4 km²) increase in the area of very high potential soil loss, from 7% of the study area in 1987 to 12% in 2003, due mainly to very high soil-loss potential in croplands. Whilst the area of croplands as a whole increased, the relative proportion of very high soil-loss potential in croplands remained 20%, both in 1987 and in 2003, indicating that newly cleared agricultural lands with vulnerable soils are the most at-risk areas.     

On the net surface water exchange rate estimated from remote-sensing observation and reanalysis

This study compares the net surface water exchange rates, or surface precipitation (P) minus evapotranspiration (ET), and atmospheric water vapour sinks calculated from various observations and reanalyses, and investigates whether they are physically consistent. We use the observed precipitation from the Global Precipitation Climatology Project (GPCP) and the Tropical Rainfall Measuring Mission (TRMM) 3B43, ocean evaporation from Goddard Satellite-based Surface Turbulent Fluxes Version 2c (GSSTF2c), and land ET from the Moderate Resolution Imaging Spectroradiometer (MODIS) global ET project (MOD16) and PT-JPL products to calculate observed P minus observed ET. P–ET is also obtained from atmospheric water vapour sink calculated using Atmospheric Infrared Sounder (AIRS)/Advanced Microwave Sounding Unit observation specific humidity observation and wind fields from the Modern-Era Retrospective Analysis for Research and Applications (MERRA) and ERA-interim, denoted as AIRS_M and AIRS_E, respectively. MERRA and ERA-interim water vapour budgets are also calculated for cross-comparison and consistency check. The period of study is between 2003 and 2006 based on the availability of all of the data sets. Averaged water vapour sinks from AIRS and reanalysis are consistent over the global ocean and are close to zero (range: 0.02–0.06 mm day^?1), but range between 0.14 and 0.23 mm day^?1 when land is included. Over ocean within 50^oS--50^oN, averaged observed P minus observed evaporation shows a much larger negative number than that obtained from AIRS and reanalysis. The differences mainly occur over subtropical oceans, especially in the southern hemisphere in summer and the northern hemisphere in winter. Over land, generally higher agreement between observed P minus observed ET and atmospheric water vapour sinks (calculated from AIRS and reanalysis) is found. However, large regional differences, often with strong seasonal dependence, are also observed over land. Estimates of atmospheric water vapour sinks are influenced by both winds and biases in water vapour data, especially over tropics and subtropical oceans, thereby calling for the need for further investigations and consistency checks of satellite-based and reanalysis water vapour, reanalysis winds, P observations, and surface evaporation estimates. In higher latitudes, atmospheric water vapour sinks calculated from AIRS_M, AIRS_E, MERRA, and ERA-interim are more consistent with each other.     

A comparison study of voxel based multi- and two-layer ionospheric tomography models over the Indian region using GPS data

Three dimensional multi- and two-layer ionospheric tomographic models have been developed for the Indian region, which may provide an alternative approach for total electron content (TEC) estimation and ionospheric propagation delay correction for user aircraft for Global Positioning System Aided Geo Augmented Navigation (GAGAN). A comparative study has been carried out between multi- and two-layer models with different voxel sizes. Further comparison of two-layer models with different voxel sizes shows that from 0006 to 0017 h UT, the root mean square error (RMSE) of the slant total electron content (STEC) is least for a 10° × 10° voxel size and for the remaining hours the RMSE of the STEC is least for the 15° × 15° voxel size. The main conclusion of the analysis is that the two-layer model performs around 50% better than the multi-layer model over the Indian region.     

Assessing the coupling between surface albedo derived from MODIS and the fraction of diffuse skylight over spatially-characterized landscapes

In this effort, the MODerate Resolution Imaging Spectroradiometer (MODIS) (Collection V005) Bidirectional Reflectance Distribution Function (BRDF)/Albedo algorithm is used to retrieve instantaneous surface albedo at a point in time and under specific atmospheric conditions. These retrievals are then used to study the role that the fraction of diffuse skylight plays under realistic scenarios of anisotropic diffuse illumination and multiple scattering between the surface and atmosphere. Simulations of the sky radiance using the MODTRAN®5.1 radiative transfer model were performed under different aerosol optical properties, illumination conditions, and surface characteristics to describe these effects on surface albedo retrievals from MODIS. This technique was examined using a validation scheme over four measurement sites with varied aerosol levels and landscapes, ranging from croplands to tundra ecosystems, and over extended time periods. Furthermore, a series of geostatistical analyses were performed to examine the types of spatial patterns observed at each measurement site. In particular, Enhanced Thematic Mapper Plus (ETM+) retrievals of surface albedo were acquired to analyze the change in variogram model parameters as a function of increased window-size. Results were then used to assess the degree to which a given point measurement is able to capture the intrinsic variability at the scale of MODIS observations. Assessments of MODIS instantaneous albedos that account for anisotropic multiple scattering, over snow-free and snow-covered lands and at all diurnal solar zenith angles, show a slight improvement over the albedo formulations that treat the downwelling diffuse radiation as isotropic. Comparisons with field measurements show biases improving by 0.004-0.013 absolute units (root-mean-squared error) or 0.1%-2.0% relative error.     

Assessing the potential of SeaWiFS and MODIS for estimating chlorophyll concentration in turbid productive waters using red and near-infrared bands

Bio-optical algorithms for remote estimation of chlorophyll-a concentration (Chl) in case-1 waters exploit the upwelling radiation in the blue and green spectral regions. In turbid productive waters other constituents, that vary independently of Chl, absorb and scatter light in these spectral regions. As a consequence, the accurate estimation of Chl in turbid productive waters has so far not been feasible from satellite sensors. The main purpose of this study was to evaluate the extent to which near-infrared (NIR) to red reflectance ratios could be applied to the Sea Wide Field-of-View Sensor (SeaWiFS) and the Moderate Imaging Spectrometer (MODIS) to estimate Chl in productive turbid waters. To achieve this objective, remote-sensing reflectance spectra and relevant water constituents were collected in 251 stations over lakes and reservoirs with a wide variability in optical parameters (i.e. 4 ≤ Chl ≤ 240 mg m^− 3; 18 ≤ Secchi disk depth ≤ 308 cm). SeaWiFS and MODIS NIR and red reflectances were simulated by using the in-situ hyperspectral data. The proposed algorithms predicted Chl with a relative random uncertainty of approximately 28% (average bias between − 1% and − 4%). The effects of reflectance uncertainties on the predicted Chl were also analyzed. It was found that, for realistic ranges of R_rs uncertainties, Chl could be estimated with a precision better than 40% and an accuracy better than ± 35%. These findings imply that, provided that an atmospheric correction scheme specific for the red-NIR spectral region is available, the extensive database of SeaWiFS and MODIS images could be used to quantitatively monitor Chl in turbid productive waters.     

Linear retrieval,validation and mapping of the sea surface temperature over western mid-latitude North Pacific using Seasat SMMR data

Abstract

The Scanning Multichannel Microwave Radiometer (SMMR) aboard the Seasat satellite measured emitted radiation in both horizontal and vertical polarizations at microwave frequencies of 6.6, 1069, 18.0, 21.0 and 37.0 GHz. Retrieval algorithms, for sea surface temperature (SST) determination, from subsets of one to three SMMR channels, are obtained by a two-step statistical technique. The technique first selects the best subsets of a given size defined by an R2 criterion (coefficient of determination), of a given size by the application of an efficient leaps and bounds technique on a statistical data base. It then performs a regression analysis on the selected subsets. The statistical data base employed a large (600) set of seasonally and geographically diverse atmospheric and surface parameters for radiative transfer calculations. The results of the study of one to three-channel subset retrieval algorithms indicate the possibility of using 6.6 V, 6.6 H and 18 V channels for SST determinatidn from Seasat-SMMR data. A comparison of SMMR-SST derived from three channels mentioned above and expendable bathythermograph (XBT) measurements over the North Pacific provided an r.m.s. difference of ～ 1.4 K which is comparable to the accuracy obtained from a five-channel subset (6 6 V, 6-6 H, 10-69 H, 180V, 21 OH) retrieval algorithm. The retrieval technique has the ability to recognize severe noise in brightness temperature measurements which may lead to unacceptable parameter retrieval. This may be achieved by setting up a quality control criteria either using different subsets of the same size or of different sizes. The three-channel retrieval compares within ～ 1.2 K with Chester's algorithm, which is being used at the Jet Propulsion Laboratory for geophysical processing.

Ten-day and monthly average SMMR-SST contour maps are produced using three-channel retrieval for the period 7 July-6 August 1978 over the western North Pacific, 20^°-50^° N, 140^°-180^° E. These contour maps are compared against similar maps obtained from Chester's algorithm and ship's observations. All the SMMR-SST maps show the major climatological features and are in reasonable agreement with ship's SST maps.